Next step in AI journey-Passing AWS Certified AI Practitioner (AIF-C01)

While preparing for the AWS Certified Cloud Practitioner exam, I came across a new certification focused on AI: AWS Certified AI Practitioner (AIF-C01). My curiosity led me to dive deeper into the field.

I use Generative AI daily, leveraging tools like ChatGPT-4 Mini and Claude Anthropic. More importantly, I have worked on projects integrating LLMs with application logic, such as personal project Cinelar, a movie recommendation system that suggests similar films based on input.

Last year, I also passed the AI_DEVS 2 certification, which was heavily focused on practical tasks involving LLM integration via APIs. This hands-on experience gave me a strong foundation in working with Generative AI. Building on that, I wanted to deepen my understanding of AWS’s AI capabilities, which led me to take the AIF-C01 exam.

Taking this exam was absolutely worth it. While learning about AWS services was valuable, the real benefit came from understanding AI concepts at a higher level, allowing me to see the bigger picture of AI.

General Concepts

AI Model Development Process (The Big Picture)

• Artificial Intelligence (AI) → Machine Learning (ML) → Deep Learning (DL) → Generative AI
  (Establishes hierarchy: AI is broadest, ML is a subset, etc.)
• Foundation Models & Large Language Models (LLMs) - Pre-trained models like GPT and Claude.
• Diffusion Models & Multi-modal Models - Used for generating images, text, and more.

Types of Machine Learning (How Models Learn)

• Supervised Learning - Model learns from labeled data.
• Unsupervised Learning - Model finds patterns in unlabeled data.
• Self-supervised Learning - Model generates labels from raw data.
• Reinforcement Learning (RL) - Model learns through rewards & trial-and-error.

Data Types & Structure (Understanding Inputs)

• Labeled vs. Unlabeled Data- Defines whether outputs are known.
• Structured vs. Unstructured Data - Defines how data is formatted (tables vs. text/images).

Data Splits (How Data is Used in Training)

• Training Set - Used to teach the model.
• Validation Set - Used for hyperparameter tuning.
• Test Set - Used to evaluate final model performance.

Bias & Variance (Core Concept for Model Performance)

• Bias - Too simple (underfitting).
• Variance - Too complex (overfitting).

Hyperparameter Tuning & Feature Engineering (Improving Model Performance)

• Hyperparameter Tuning - Adjusting parameters like learning rate, batch size, number of layers.
• Feature Engineering - Transforming raw data into better input features.

Model Evaluation Metrics (How to Measure Model Success)

• Accuracy - Percentage of correct predictions.
• Precision & Recall - Important for imbalanced datasets.
• F1-score - Balance between precision & recall.
• ROC-AUC - Measures model discrimination ability.

Prompt Engineering (Specific to LLMs & Generative AI)

• Chain of Thought (CoT) - Guides models through reasoning.
• Few-shot Learning - Providing examples in the prompt.
• Zero-shot Learning - Asking the model to perform tasks without examples.
• Temperature - Controls randomness.
• Top-p & Top-k - Limits token selection for controlled outputs.
• RAG (Retrieval-Augmented Generation) - Enhancing responses with external knowledge.

Learning Techniques by Data Type

For Structured Data:

• Regression - Predicts continuous values (e.g., stock prices).
• Classification - Assigns labels to categories (e.g., spam detection).

For Unstructured Data:

• Clustering - Groups similar data points (e.g., customer segmentation).
• Association Rule Learning - Finds relationships (e.g., market basket analysis).
• Anomaly Detection - Identifies rare events (e.g., fraud detection).

AWS AI/ML Services

Core AI/ML Services

• Amazon Bedrock - Provides access to foundation models for generative AI, making it easier to integrate AI capabilities into applications.
• Amazon SageMaker - End-to-end ML platform offering model building, training, tuning, and deployment.
• AWS Q - AI-powered enterprise assistant for code generation and knowledge retrieval.

Managed AI Services

These services offer pre-built AI capabilities without requiring deep ML expertise:

• Amazon Comprehend - NLP service for sentiment analysis, entity recognition, and key phrase extraction.
• Amazon Translate -Real-time and batch text translation across multiple languages.
• Amazon Textract - Extracts text, handwriting, and data from scanned documents.
• Amazon Rekognition - Image and video analysis, including object detection and facial recognition.
• Amazon Kendra - AI-powered enterprise search for improving information retrieval.
• Amazon Lex - Conversational AI for building chatbots and virtual assistants.
• Amazon Polly - Converts text to lifelike speech using neural TTS models.
• Amazon Transcribe - Automatic speech recognition (ASR) for transcribing audio files.
• Amazon Personalize - AI-driven recommendation system similar to those used by e-commerce platforms.

My Study Resources

• Ultimate AWS Certified AI Practitioner (AIF-C01) - Stephan Maarek’s Course - A great knowledge source, highly recommend revisiting the slides to reinforce key concepts.
• Practice Exams - Stephan Maarek’s Practice Tests -Includes four practical exams, slightly harder than the real exam, making them excellent preparation.
• AWS Skill Builder - AWS’s official training platform, providing structured learning paths and exam prep resources.

Final Thoughts

The AIF-C01 exam helped me strengthen my AI/ML expertise within AWS. If you’re looking to expand your cloud-based AI knowledge, this certification is a great step forward.